add conferences

data_extraction/build_html.py

@@ -194,7 +194,7 @@ def get_published_at():
                 elif "European Conference on Computer Vision" in pub:
                     pub = "ECCV"
                 elif "ACM on Computer Graphics and Interactive Techniques" in pub:
-                    pub = "PACMCGIT"
+                    pub = "I3D"
                 elif "ACM Transactions on Graphics" in pub:
                     pub = "TOG"
 

methods.bib

@@ -2,6 +2,7 @@ @misc{morgenstern2024compact
       title={Compact 3D Scene Representation via Self-Organizing Gaussian Grids}, 
       author={Wieland Morgenstern and Florian Barthel and Anna Hilsmann and Peter Eisert},
       year={2024},
+      booktitle={European Conference on Computer Vision},
       eprint={2312.13299},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
@@ -24,7 +25,8 @@ @misc{lee2024compact
 @misc{navaneet2023compact3d,
       title={Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization}, 
       author={KL Navaneet and Kossar Pourahmadi Meibodi and Soroush Abbasi Koohpayegani and Hamed Pirsiavash},
-      year={2023},
+      year={2024},
+      booktitle={European Conference on Computer Vision},
       eprint={2311.18159},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
@@ -49,6 +51,7 @@ @misc{chen2024hac
       title={HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression}, 
       author={Yihang Chen and Qianyi Wu and Jianfei Cai and Mehrtash Harandi and Weiyao Lin},
       year={2024},
+      booktitle={European Conference on Computer Vision},
       eprint={2403.14530},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
@@ -114,7 +117,7 @@ @misc{sun2024f3dgs
       archivePrefix={arXiv},
       primaryClass={id='cs.CV' full_name='Computer Vision and Pattern Recognition' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.'},
       abstract={The neural radiance field (NeRF) has made significant strides in representing 3D scenes and synthesizing novel views. Despite its advancements, the high computational costs of NeRF have posed challenges for its deployment in resource-constrained environments and real-time applications. As an alternative to NeRF-like neural rendering methods, 3D Gaussian Splatting (3DGS) offers rapid rendering speeds while maintaining excellent image quality. However, as it represents objects and scenes using a myriad of Gaussians, it requires substantial storage to achieve high-quality representation. To mitigate the storage overhead, we propose Factorized 3D Gaussian Splatting (F-3DGS), a novel approach that drastically reduces storage requirements while preserving image quality. Inspired by classical matrix and tensor factorization techniques, our method represents and approximates dense clusters of Gaussians with significantly fewer Gaussians through efficient factorization. We aim to efficiently represent dense 3D Gaussians by approximating them with a limited amount of information for each axis and their combinations. This method allows us to encode a substantially large number of Gaussians along with their essential attributes -- such as color, scale, and rotation -- necessary for rendering using a relatively small number of elements. Extensive experimental results demonstrate that F-3DGS achieves a significant reduction in storage costs while maintaining comparable quality in rendered images.},
-      url={https://arxiv.org/abs/2405.17083}, 
+      url={https://xiangyu1sun.github.io/Factorize-3DGS/}, 
       shortname={F-3DGS},
 }
 
@@ -133,6 +136,7 @@ @misc{wang2024end
       title={End-to-End Rate-Distortion Optimized 3D Gaussian Representation}, 
       author={Henan Wang and Hanxin Zhu and Tianyu He and Runsen Feng and Jiajun Deng and Jiang Bian and Zhibo Chen},
       year={2024},
+      booktitle={European Conference on Computer Vision},
       eprint={2406.01597},
       archivePrefix={arXiv},
       primaryClass={cs.CV},